Regardless of its core mission, every organization needs to effectively manage its business information, and that consists of more than just accumulating data. Effective information management involves understanding the business context, modeling and planning the data structures, and establishing an infrastructure for using the data. Managed information is a strategic resource—it unlocks business value by revealing trends, promoting business intelligence, and improving decision-making.
Effective information management depends on quality data, maintained in a data management system (DMS). Without a systematic approach, business information is often collected in an unstructured, undocumented way, leading to unreliable or unusable data. Quality issues may be latent for a long time. Without an investment in design and planning, a host of issues can develop—issues that can undermine business reporting and decision making. Often, problems go unnoticed until a critical audit or compliance report cannot be delivered.
The benefits of a DMS
The best way to avoid setbacks is to design and implement an information system at the beginning of a new project or process. An up-to-date, structured data management system provides a systematic way to analyze data and report on a variety of important business statistics and operational information, including
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project status (schedule, budget, targets, performance ranges)
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operations monitoring (thresholds, min/max, variance from normal)
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activity monitoring (work scheduled, work performed, issues, complaints tracking)
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regulatory compliance reporting (statutory information collected from HR, finance, health and safety records, etc.)
Sometimes highly specialized, commercial data management systems are best suited; sometimes a custom-designed system will better meet the data management needs. Correct analysis and assessment of business needs determine the make or buy outcome.
Regardless of the requirements—whether it be supporting a new project's reporting needs, extracting business knowledge from operational data, or re-engineering a chaotic or mismanaged system—successful data management begins with the design of a proper information model. The information model is composed of the structural components that describe the items of interest (the data); the integrity components, which ensure data validity; and the manipulation components, which control how the data is accessed and updated.
Putting a system in place
Information modeling manifests in three stages. During the first stage, conceptual model design, the database manager collects, identifies, and integrates information and documents data attributes, relationships, interactions, and views. Data views are then integrated into a single conceptual model description, which is refined into the logical data model.
In the second stage, the logical model abstracts groups of related information into system representations, which become the structural plan for data items such as tables, columns, primary and foreign keys, relationships, rules, and other constraints. The outcome is a blueprint for the physical data model, which actually implements the logical model.
The final stage, information modeling, is a technical task, which uses a computer system to physically define the database and its files, fields, and physical structures, and will contain real data in a commercial relational database management system.
A well-designed and maintained information system is an investment in current and future business health and has intrinsic value to an organization. Nevertheless, in order for a data management system to be effective, the design must begin as soon as you anticipate long-lasting value in a project's operational data…and that occurs long before the data is collected.